3 Video Content Management 4 Image Retrieval Current Practice 5 Image Retrieval Current Practice 6 Image Retrieval Current Practice 7 Precision Recall 8 Multi-level Modeling 9 Features background composition color texture face recognition motion text rec. OCR shape closed caption berg-etappe etappe nr 17 speach rec. 10 Query _at_ feature level 11 QBIC 12 Amore 13 Video Retrieval 14 Video Retrieval Modeling 15 Video Retrieval Modeling 16 Video Retrieval Modeling 17 Video Retrieval Modeling 18 Video Retrieval Query 19 Video Retrieval Query 20 Video Retrieval Query 21 Video Retrieval Query 22 The Semantic Gap User Concepts Semantic gap Raw Video Data Data 23 Tennis Goal Video Shot segmentation and classification Shots (1) Playing (2) Close up (3) Audience and other 24 Tennis HMM History
50s - statisticians tried to characterize processes with incomplete observations.
60s - Baum et al. published HMM theory in a series of papers.
70s - HMMs were used at IBM and CMU for speech recognition.
90s - widespread applications (Gesture recognition human actions)
25 Tennis HMM example 26 Tennis HMM example 27 Tennis Pre-processing initial segmentation (robust M-estimator) Step 1 Step 2 Step 3 locate player fit 3D model final segmentation 28 Tennis Features Shape Pie features Skeleton features 29 Tennis HMM Feature extraction Time Time 30 Tennis HMM . . time states 31 Tennis Experiments
Determine the right feature set
Investigate person independence
Two series of experiments
Same player in the training and evaluation
Training with one player evaluation of strokes performed by various players
Six events Forehand Backhand Service Smash Forehand volley and Backhand volley
33 Tennis System Screen Dump 34 Tennis Some Performance Figures Dual Pentium II 350 MHz Preprocessing 6.5 min. per stroke of 45 frames initial segmentation 95sec fitting 3D models 150 sec final segmentation 105 sec feature extraction 40 sec Training the model 20 sequences of 45 frames 50 iterations Baum-Welch 4 states 8 symbol codebook 12 sec 48 states 80 symbols 20 min. Inference between 1 and 30 sec depending on model complexity in our case for 8 states model with 20 symbols 2 sec 35 Formula-1 Goal 36 Formula-1 Bayesian Networks A B C D E P(XY) P(XY) P(Y) P(XY)P(Y) P(YX)P(X) P(X1X2Xn) P i1..n P(XiPa(Xi)) 37 Formula-1 Audio BN Kword Key Words PRate Pause Rate AV Average Values DR Dynamic Range MV Maximum Values STE Short Time Energy MFCC Mel-Frequency Cepstral Coefficients 38 Formula-1 Audio DBNs 39 Formula-1 Effect of Audio DBNs
40 Formula-1 Example Videos 41 Formula-1 Highlights on Audio 42 Formula-1 Combined Audio-Video 43 Formula-1 Experimental Results 44 Formula-1 System Screen Dump 45 Formula-1 Some Performance Figures 46 Bridging the Gap User Concepts Domain knowledge Domain features Features Raw Video Data Data 47 DMW projecthttp//wwwhome.cs.utwente.nl/dmw/ 48 Some Papers
M. Petkovic W. Jonker Content-Based Video Retrieval by Integrating Spatio-Temporal and Stochastic Recognition of Events In Proc. IEEE International Workshop on Detection and Recognition of Events in Video Vancouver Canada July 2001.
M.Petkovic W. Jonker Z. Zivkovic Recognizing Strokes in Tennis Videos Using Hidden Markov Models International Conference on Visualization Imaging and Image Processing Marbella Spain September 3-5 2001.
H. E. Blok M. Windhouwer R. Zwol M. Petkovic P. M. G. Apers W. Jonker M. Kersten Flexible and Scalable Digital Library Search 27th International Conference on Very Large Databases Roma Italy September 11-14 2001.
V. Mihajlovic M. Petkovic W. Jonker Content-Based Retrieval of TV Formula 1 Programs 2nd International Workshop on Multimedia Data Document Engineering (MDDE02) March 24 2002 Prague
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